new ratio code

train_network.py

@@ -345,10 +345,48 @@ class NetworkTrainer:
         # DataLoaderのプロセス数：0はメインプロセスになる
         n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
 
+        def get_indices_without_reg(dataset: torch.utils.data.Dataset):
+            return [id for id, (key, item) in enumerate(dataset.image_data.items()) if item.is_reg is False]
+
+        from typing import Sequence, Union
+        from torch._utils import _accumulate
+        import warnings
+        from torch.utils.data.dataset import Subset
+
+        def random_split(dataset: torch.utils.data.Dataset, lengths: Sequence[Union[int, float]]):
+            indices = get_indices_without_reg(dataset)
+            random.shuffle(indices)
+
+            subset_lengths = []
+
+            for i, frac in enumerate(lengths):
+                if frac < 0 or frac > 1:
+                    raise ValueError(f"Fraction at index {i} is not between 0 and 1")
+                n_items_in_split = int(math.floor(len(indices) * frac))
+                subset_lengths.append(n_items_in_split)
+
+            remainder = len(indices) - sum(subset_lengths)
+
+            for i in range(remainder):
+                idx_to_add_at = i % len(subset_lengths)
+                subset_lengths[idx_to_add_at] += 1
+
+            lengths = subset_lengths
+            for i, length in enumerate(lengths):
+                if length == 0:
+                    warnings.warn(f"Length of split at index {i} is 0. "
+                                  f"This might result in an empty dataset.")
+
+            if sum(lengths) != len(indices):
+                raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
+
+            return [Subset(dataset, indices[offset - length: offset]) for offset, length in zip(_accumulate(lengths), lengths)]
+
+
         if args.validation_ratio > 0.0:
             train_ratio = 1 - args.validation_ratio
             validation_ratio = args.validation_ratio
-            train, val = torch.utils.data.random_split(
+            train, val = random_split(
                 train_dataset_group,
                 [train_ratio, validation_ratio]
             )
@@ -358,6 +396,8 @@ class NetworkTrainer:
             train = train_dataset_group
             val = []
 
+
+
         train_dataloader = torch.utils.data.DataLoader(
             train,
             batch_size=1,
@@ -898,7 +938,7 @@ class NetworkTrainer:
 
                 if args.logging_dir is not None:
                     logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)
-                    accelerator.log(logs, step=global_step)
+                    accelerator.log(logs)
 
                 if global_step >= args.max_train_steps:
                     break
@@ -973,13 +1013,11 @@ class NetworkTrainer:
                     loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
                     current_loss = loss.detach().item()
-
                     val_loss_recorder.add(epoch=epoch, step=val_step, loss=current_loss)
 
                 if len(val_dataloader) > 0:
-                    avr_loss: float = val_loss_recorder.moving_average
-
                     if args.logging_dir is not None:
+                        avr_loss: float = val_loss_recorder.moving_average
                         logs = {"loss/validation": avr_loss}
                         accelerator.log(logs, step=epoch + 1)